This is an R Markdown Notebook. When you execute code within the notebook, the results appear beneath the code.

Try executing this chunk by clicking the Run button within the chunk or by placing your cursor inside it and pressing Ctrl+Shift+Enter. # First part ## data filter and normalization

source("./tianfengRwrappers.R")
载入需要的程辑包:dplyr

载入程辑包:‘dplyr’

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    count

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    combine

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    intersect, setdiff, union

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    intersect

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    collapse, desc, intersect, setdiff, slice, union

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    first, intersect, rename, setdiff, setequal, union

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    combine, intersect, setdiff, union

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    filter, lag

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    intersect, setdiff, setequal, union

载入需要的程辑包:reticulate
载入需要的程辑包:tidyr

载入程辑包:‘tidyr’

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    expand


载入程辑包:‘MySeuratWrappers’

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    DimPlot, DoHeatmap, LabelClusters, RidgePlot, VlnPlot


载入程辑包:‘cowplot’

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    get_legend

载入需要的程辑包:viridisLite

载入程辑包:‘reshape2’

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NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
      Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and
      if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow

Registered S3 method overwritten by 'enrichplot':
  method               from
  fortify.enrichResult DOSE
clusterProfiler v3.14.3  For help: https://guangchuangyu.github.io/software/clusterProfiler

If you use clusterProfiler in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.

载入程辑包:‘clusterProfiler’

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Registering fonts with R

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========================================
circlize version 0.4.13
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/

If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
  in R. Bioinformatics 2014.

This message can be suppressed by:
  suppressPackageStartupMessages(library(circlize))
========================================

载入需要的程辑包:grid
========================================
ComplexHeatmap version 2.2.0
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference

If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
  genomic data. Bioinformatics 2016.
========================================


载入程辑包:‘ComplexHeatmap’

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    add_heatmap

冠状动脉

dataset from Nat. Med.

human_coronary_countmatrix <- read.csv("GSE131778_human_coronary_scRNAseq.txt", sep = "\t")
func <- function(s) {
  paste0(strsplit(s, ".", fixed = T)[[1]][2], "_", strsplit(s, ".", fixed = T)[[1]][1])
}
colnames(human_coronary_countmatrix) <- lapply(colnames(human_coronary_countmatrix), func) # 拆分样本
human_coronary <- CreateSeuratObject(counts = human_coronary_countmatrix, 
                                     project = "human_coronary", min.cells = 10, min.features = 300) %>% 
    PercentageFeatureSet(pattern = "^MT-", col.name = "percent.mt") %>%
    subset(subset = nFeature_RNA > 600 & nFeature_RNA < 6000 & nCount_RNA > 1000 &  nCount_RNA < 30000) %>%
    SCTransform(vars.to.regress = "percent.mt", verbose = F) %>% 
    RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
    RunUMAP(dims = 1:20) %>% 
    FindClusters(resolution = 0.1)
rm(human_coronary_countmatrix)

颈动脉斑块 CA dataset1

# 批量读取计数矩阵
# 需要把行名的gene删掉,用vscode修改
count_mats <- list.files("./CA_GSE155512")
count_mats <- count_mats[count_mats != "sampleinfo.txt"]
allList <- lapply(count_mats, function(folder) {
  CreateSeuratObject(
    counts = read.csv(paste0("./CA_GSE155512/", folder), sep = "\t"),
    project = folder, min.cells = 10, min.features = 300
  )
})
# 合并seurat对象
CA_dataset1 <- merge(allList[[1]],
  y = allList[-1], add.cell.ids = count_mats,
  project = "CA_dataset1"
)
rm(allList)

CA_dataset1 <- PercentageFeatureSet(CA_dataset1, pattern = "^MT-", col.name = "percent.mt") %>%
    subset(subset = nFeature_RNA > 600 & nFeature_RNA < 6000 & nCount_RNA > 1000 &  nCount_RNA < 30000) %>%
    SCTransform(vars.to.regress = "percent.mt", verbose = F) %>% 
    RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
    RunUMAP(dims = 1:20) %>% 
    FindClusters(resolution = 0.1)

颈动脉斑块 CA dataset2

CA_dataset2 <- CreateSeuratObject(Read10X("./CA_GSE159677/"), names.field = 2, names.delim = "-",
                                   project = "CA_dataset2", min.cells = 10, min.features = 300) %>% 
  PercentageFeatureSet(pattern = "^MT-", col.name = "percent.mt") %>%
  subset(subset = nFeature_RNA > 600 & nFeature_RNA < 6000 & nCount_RNA > 1000 &  nCount_RNA < 30000) %>%
  SCTransform(vars.to.regress = "percent.mt", verbose = F) %>% 
  RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
  RunUMAP(dims = 1:20) %>% 
  FindClusters(resolution = 0.1)

添加metadata samples存储完整信息,conditions按区域分,groups按病例分

Idents(human_coronary) <- human_coronary$orig.ident
Idents(human_coronary) <- c("1","1","2","2","3","3","4","4")
human_coronary$samples <- Idents(human_coronary)
Idents(human_coronary) <- human_coronary$seurat_clusters

Idents(CA_dataset2) <- CA_dataset2$orig.ident
CA_dataset2 <- RenameIdents(CA_dataset2,'1' = 'AC_1','2' = 'PA_1','3' = 'AC_2','4' = 'PA_2','5' = 'AC_3','6' = 'PA_3')
UMAPPlot(CA_dataset2)

CA_dataset2$sample <- Idents(CA_dataset2)
CA_dataset2 <- RenameIdents(CA_dataset2,'AC_1' = 'AC','PA_1' = 'PA','AC_2'= 'AC','PA_2'= 'PA','AC_3'= 'AC','PA_3'= 'PA')
CA_dataset2$conditions <- Idents(CA_dataset2)
Idents(CA_dataset2) <- CA_dataset2$orig.ident
CA_dataset2 <- RenameIdents(CA_dataset2, '1' = 'sp_1','2' = 'sp_1','3' = 'sp_2','4' = 'sp_2','5' = 'sp_3','6' = 'sp_3')
CA_dataset2$groups <- Idents(CA_dataset2)
Idents(CA_dataset2) <- CA_dataset2$seurat_clusters

保存结果

saveRDS(human_coronary,"human_coronary.rds")
saveRDS(CA_dataset1,"CA_dataset1.rds")
saveRDS(CA_dataset2,"CA_dataset2.rds") #已经经过分组处理了
Idents(CA_dataset2) <- CA_dataset2$conditions
AC <- subset(CA_dataset2, idents = "AC")
PA <- subset(CA_dataset2, idents = "PA")
AC <- AC %>% RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
  RunUMAP(dims = 1:20) %>% 
  FindClusters(resolution = 0.1)
PA <- PA %>% RunPCA() %>% FindNeighbors(dims = 1:20) %>% 
  RunUMAP(dims = 1:20,seed.use = 20) %>% 
  FindClusters(resolution = 0.1)
AC <- AC %>% RunUMAP(dims = 1:20,seed.use = 20) %>% 
  FindClusters(resolution = 0.1)
SMC <- subset(AC,idents = c(1,4))
SMC <- SMC %>% FindNeighbors(dims = 1:20) %>% FindClusters(resolution = 0.2)
Computing nearest neighbor graph
Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 3057
Number of edges: 98364

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.9273
Number of communities: 5
Elapsed time: 0 seconds
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